Novelty Classification Model Use in Reinforcement Learning for Cervical Cancer
The integration of CNNs, ViTs, and RL into RL-CancerNet significantly improves the diagnostic accuracy of cervical cancer screenings. This model not only advances the field of automated medical screening but also provides a scalable framework adaptable to other medical imaging tasks, potentially enhancing diagnostic processes across various medical domains.
Authors
- Sabina Umirzakova (ORCID: https://orcid.org/0000-0002-7694-1806)
- Shakhnoza Muksimova (ORCID: https://orcid.org/0000-0002-6223-4502)
- Young Im Cho (ORCID: https://orcid.org/0000-0003-0184-7599)
- Khusanboy Shoraimov
- Jushkin Baltayev
Institutions
- Gachon University (KR)
- Tashkent University of Information Technology (UZ)
- Tashkent State University of Economics (UZ)
Publication Details
- Journal
- Cancers
- Published
- 2024-11-10
- DOI
- https://doi.org/10.3390/cancers16223782
- Citations
- 23
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 7.99
Funders
- Ministry of Trade, Industry and Energy